High-resolution　remote　sensing　images　can　capture　detailed　geometrical　and　shape　properties.　Traditional　classification　accuracy　assessments　with　overall　accuracy　or　kappa　coefficient　based　on　pixels,　cannot　exhibit　the　geometrical　properties　of　the　objects　that　are　present　on　the　ground.　Evaluation　of　object　oriented　classified　maps　based　on　geometrical　and　border　information　can　provide　more　accurate　results.　In　this　paper,　we　introduced　and　improved　some　object-based　indices　to　evaluate　the　classification　accuracy　of　the　thematic　maps　obtained　by　high-resolution　images.　The　indices　depend　on　the　geometry　features　of　each　object　of　the　thematic　map　based　on　geometric　error,　including　over　segmentation,　under　segmentation,　edge　location,　fragmentation　error　and　shape　error.　Experiments　conducted　on　Quickbird　image　in　Fuzhou　city　show,　compared　to　the　traditional　pixel-based　accuracy　assessment,　our　improved　indices　can　provide　more　an　accurately　and　quantitatively　accurate　evaluation　of　each　land　cover　class,　and　can　conduct　more　effectively　for　users　to　choose　the　best　classification　map.　©　2011　IEEE.